ArticleBiomedical optics express2025
Lightweight denoising speckle contrast image GAN for real-time denoising of laser speckle imaging of blood flow.
Article in Biomedical optics express, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Green channel-informed collaborative filtering for high-fidelity laser speckle contrast imaging in dual-display laparoscopy.Biomedical optics express · 2026Article
- The Diagnosis and Treatment Technology of Port-Wine Stain, and the Prospect of Future Treatment.Dermatology and therapy · 2026Review
- Using the latent diffusion model to enhance time-resolved laser speckle contrast imaging (TR-LSCI) of cerebral blood flow.Biomedical optics express · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To tackle real-time denoising of noisy laser speckle blood flow images, a novel lightweight denoising speckle contrast image generative adversarial network (LDSCI-GAN) is proposed. In the framework, a lightweight denoiser removes noise from the original image, and a discriminator compares the denoised result with the reference one, enabling efficient learning and optimization of the denoising process. With a multi-scale loss function in the log-transformed domain, the training process significantly improves accuracy and denoising by using only five frames of raw speckle images while well-preserving the overall pixel distribution and vascular contours. Animal and phantom experimental results indicate that the LDSCI-GAN can eliminate vascular artifacts while retaining the accuracy of relative blood flow velocity. In terms of peak signal-to-noise ratio (PSNR), mean structural similarity index (MSSIM), and Pearson correlation coefficient (R), the LDSCI-GAN outperforms other deep-learning methods by 3.07 dB, 0.10 (
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Registered trials
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